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By Stephen G.Hartke

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Quasi-online learning is more efficient than the BPTT algorithm [8]. According to this, the RTRL algorithm is used in this paper. Therefore we make use of the developed formulas of the layered digital dynamic network. The interested reader is referred to [5]-[8] for further details. 4 Architecture Selection Pruning algorithms [1]–[4] have initially been designed for static neural networks to obtain good generalization. 2 we do not have overtraining problems. This paper is focused on the question: Is it possible to find the optimal structure of a nonlinear dynamic system?

The simple linear system identification example is chosen to show the weight reduction ability. The input weight of the final GDNN is very small to get an almost linear behavior of the model in the system’s operating range. In addition to that the weights of the GDNN model correspond very closely to the coefficients of the according difference equation. If the GDNN is too small, the error increases with the pruning process. This is shown in the nonlinear plant identification example. In that case the user has to know the quality demand of the final GDNN model.

Also note that rules 11 and 12 cause a direct contradiction with rule 8 for good wines. The semantics based on the causal rejection of rules deals with this issue. Also worth noting is that, for each recommendation, there is an explanation based on user rules, on owner rules, or on the initial recommendation (as is the case of product d7 ). This and other properties are explored in the next Section. 5 Properties The use of formal semantics enables a rigorous account of its behaviour. In this Section we discuss some properties of the Stable Recommendation Semantics.

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A Survey of Free Math Fonts for TEX and LATEX by Stephen G.Hartke

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